Similar to [1]}, the training objective for the discriminator takes the synthesized image and the mismatched images as negative samples. To keep gradient smooth, we use hinge loss with MA-GP [2]} on real and match text pair. The precise formulation of the training objective of the discriminator is formulated as follows:
\(\begin{aligned}\mathcal {L}_{\text{adv }}^{D}=& \mathbb {E}_{x \sim p_{\text{data }}}[\max (0,1-D(x, s))] \\&+\frac{1}{2} \mathbb {E}_{x \sim p_{G}}[\max (0,1+D(\hat{x}, s))] \\&+\frac{1}{2} \mathbb {E}_{x \sim p_{\text{data }}}[\max (0,1+D(x, \hat{s}))],\end{aligned}\) 
